针对时变负载下,笼型电机定子电流呈现出非平稳、非周期性,使得现有时变负载下的转子断条故障识别方法受电流基频调制影响而出现诊断失效的问题,提出了 一种基于提格-凯撒能量算子(Teager-Kaiser energy operator,TKEO)的时变负载下笼型电机转子断条故障诊断方法.该方法采用TKE O提取电流信号的瞬时频率,以判断电机断条故障的严重程度.为验证该方法的有效性和优越性,通过实验获取不同健康状态和不同负载条件下的电流信号,并对其进行诊断.同时将分析结果与传统方法进行了多维度的比较.结果表明:该方法在时变负载下能够更准确地区分电机的健康状态,对负载扰动具有更强的鲁棒性,特别是轻载运行状态下,该方法的相对变化梯度数值差异相较于传统方法高4～6倍,此外,提取到的故障频率波动范围更集中在0～5 Hz之间.
Diagnosis of broken rotor bars in cage motors under time-varying loads
Under time-varying loads,the stator current of cage motors shows non-stationary and non-periodic,which makes the existing rotor broken bar fault identification method under time-varying loads fail to diagnose due to the influence of the fundamental frequency modulation of the current.This paper proposes a time-varying load cage motor diagnostic method based on the Teager-Kaiser energy operator(TKEO),a new method for rotor broken bar fault diagnosis.The method used TKEO to extract the instantaneous frequency of the current signal to determine the severity of the broken bar fault of the motor.In order to verify the effectiveness and superiority of the method,the current signals under different health states and different load conditions were ex-perimentally obtained and diagnosed.The analysis results were also compared with the traditional method.In multiple dimensions.The results show that this method is able to distinguish the health state of the motor more accurately under time-varying loads,and has stronger robustness to load disturbances;especially under light-load operation,the numerical difference of the relative change gradient of the method is 4～6 times higher in comparison with that of the traditional method.In addition,the extracted fluctuation range of the fault frequency is more concentrated in the range between 0～5 Hz.
万方数据
维普
